Solitude-Analysis

1 Data upload

2 Time management

3 ND removed, Converted to a Dataframe

#replace ND with 0

tr <- matrix(data = NA, ncol = ncol(dt[,c(1:46)]), nrow=nrow(dt))
colnames(tr) <- colnames(dt[,c(1:46)])
for (i in 12:46)
{
  tr[,c(i)] <- gsub(".*ND.*", 0, dt[,i])
}

for(i in 1:11)
{
  tr[,c(i)] <- dt[,c(i)]
}
   

#transform to dataframe
tr <- as.data.frame.matrix(tr) #A correct command to change the dataset to dataframe after transformations
tr[,12:46] <- sapply(tr[,12:46],as.numeric) # Change a character to numeric (double)
typeof(tr$Cu_concentration) # confirm the value is no longer a character

## [1] "double"

4 Head of the dataset

head(tr)

Data frame is now printed using kable.

Scientific_Name	Group	Plot	Sample_Name	Tube_No	Type_of_Sample	Total_Weight	Cup_No	pXRF_measurement_ID	File	Material	Cl_concentration	Cl_uncertainty	Ca_concentration	Ca_uncertainty	Ti_concentration	Ti_uncertainty	Cr_concentration	Cr_uncertainty	Mn_concentration	Mn_uncertainty	Fe_concentration	Fe_uncertainty	Co_concentration	Co_uncertainty	Ni_concentration	Ni_uncertainty	Cu_concentration	Cu_uncertainty	Zn_concentration	Zn_uncertainty	As_concentration	As_uncertainty	Se_concentration	Se_uncertainty	Cd_concentration	Cd_uncertainty	Re_concentration	Re_uncertainty	Hg_concentration	Hg_uncertainty	Tl_concentration	Tl_uncertainty	Pb_concentration	Pb_uncertainty	Substrate_RT
Allionia incarnata	G3	P1	P1_29_1	30;31	leaf	0.55	17	2126	Scan2126_19.14.hdx	Plant	580	268	48102	704	143.0	27.5	7.7	5.5	36.2	10.0	1037	33.0	0	2.5	0	1.2	37.5	4.5	0.0	1.1	5.0	0.3	1.0	0.6	0.0	2.5	0.0	1.5	0	0.4	0	0.6	0	0.9	0.0575852
Allionia incarnata	G3	P1	P1_30	28	leaf	0.166	18	2127	Scan2127_19.19.hdx	Plant	306	178	22621	439	132.0	22.5	4.8	4.0	47.8	10.7	1709	46.9	0	3.3	0	1.2	8.1	3.2	11.8	3.8	2.5	1.5	0.0	0.6	0.0	4.7	0.0	2.1	0	0.6	0	0.7	0	2.5	0.0143636
Allionia incarnata	G3	P1	P1_29_1_2	29	leaf	0.213	19	2128	Scan2128_19.25.hdx	Plant	527	259	47147	698	124.0	25.7	10.9	6.2	25.2	9.1	866	30.8	0	2.3	0	1.2	35.9	5.0	0.0	1.2	6.5	0.4	0.8	0.6	0.0	3.8	0.0	1.9	0	0.5	0	0.5	0	1.0	0.0345624
Allionia incarnata	G3	P2	P2_E12	33;34	leaf	0.332	20	2129	Scan2129_19.30.hdx	Plant	2576	462	37856	611	146.0	26.3	10.9	6.0	30.7	10.0	1320	35.8	0	2.5	0	1.2	47.1	5.1	0.0	1.8	6.2	0.4	2.6	0.7	0.0	4.5	4.6	2.6	0	0.5	0	0.5	0	0.8	0.0542008
Allionia incarnata	G3	P2	P2_E12_1	32	leaf	0.183	21	2130	Scan2130_19.35.hdx	Plant	4756	619	29095	530	90.3	20.5	10.6	5.6	0.0	5.8	668	26.6	0	2.0	0	1.2	26.1	4.6	0.0	1.5	4.4	1.2	2.7	1.0	20.1	20.1	6.4	3.4	0	0.7	0	0.9	0	1.9	0.0248243
Allionia incarnata	G3	P2	P2_E12_2	26	leaf	0.164	22	2131	Scan2131_19.39.hdx	Plant	753	234	6209	233	30.6	11.5	0.0	2.5	0.0	5.1	371	23.4	0	2.2	0	1.5	7.9	3.2	0.0	1.7	3.3	1.6	3.6	1.5	0.0	14.2	0.0	2.9	0	0.8	0	1.2	0	2.8	0.0110434

5 Subsets and wrangling

#Filtering with tydeverse library
dt_plants <- filter(tr,  Scientific_Name != 'QA_Sample')

P1 <- filter(dt_plants, Plot == "P1")
P2 <- filter(dt_plants, Plot == "P2")
P5 <- filter(dt_plants, Plot == "P5")
P6 <- filter(dt_plants, Plot == "P6")
P125 <- filter(dt_plants, Plot != "P6")

Se_best <- subset(dt_plants, Scientific_Name == 'Isocoma cf. tenuisecta' | Scientific_Name == 'Populus fremontii' | Scientific_Name == 'Senegalia (Acacia) greggii' )

Re_best <- subset(dt_plants, Scientific_Name == 'Isocoma cf. tenuisecta' | Scientific_Name == 'Baccharis sarothroides' | Scientific_Name == 'Senegalia (Acacia) greggii'| Scientific_Name == 'Nultuma (Prosopis) velutina' | Scientific_Name == 'Mimosa biuncifera (=aculeaticarpa)' | Scientific_Name == 'Fraxinus velutina'| Scientific_Name == 'Datura wrightii' )


# Dropping uncertainty columns for PCA analysis

dt_plants_nounc = select(dt_plants, -Cl_uncertainty,-Ca_uncertainty, -Ti_uncertainty,
                         -Cr_uncertainty, -Mn_uncertainty, -Fe_uncertainty, -Ni_uncertainty, -Cu_uncertainty,
                         -Zn_uncertainty, -As_uncertainty, -Se_uncertainty, -Cd_uncertainty, -Re_uncertainty, -Hg_uncertainty, -Co_uncertainty,
                         -Tl_uncertainty, -Pb_uncertainty, -Substrate_RT)

dt_plants_nounc = select(dt_plants_nounc, -Hg_concentration, -Tl_concentration, -Pb_concentration, -Ni_concentration, -Co_concentration)


#Filtering plants By Plot with subset function

dt_plants_nounc1 <- subset(dt_plants_nounc, Plot=="P1")
dt_plants_nounc2 <- subset(dt_plants_nounc, Plot=="P2")
dt_plants_nounc5 <- subset(dt_plants_nounc, Plot=="P5")
dt_plants_nounc6 <- subset(dt_plants_nounc, Plot=="P6")
dt_plants_nounce15 <- subset(dt_plants_nounc, Plot=="P1" | Plot=="P5")
dt_plants_nounce125 <- subset(dt_plants_nounc, Plot=="P1" | Plot=="P5" | Plot=="P2")

#Removing _concentration from column names

colnames(dt_plants_nounce125)[12] <- "Cl"
colnames(dt_plants_nounce125)[13] <- "Ca"
colnames(dt_plants_nounce125)[14] <- "Ti"
colnames(dt_plants_nounce125)[15] <- "Cr"
colnames(dt_plants_nounce125)[16] <- "Mn"
colnames(dt_plants_nounce125)[17] <- "Fe"
colnames(dt_plants_nounce125)[18] <- "Cu"
colnames(dt_plants_nounce125)[19] <- "Zn"
colnames(dt_plants_nounce125)[20] <- "As"
colnames(dt_plants_nounce125)[21] <- "Se"
colnames(dt_plants_nounce125)[22] <- "Cd"
colnames(dt_plants_nounce125)[23] <- "Re"

colnames(dt_plants_nounc6)[12] <- "Cl"
colnames(dt_plants_nounc6)[13] <- "Ca"
colnames(dt_plants_nounc6)[14] <- "Ti"
colnames(dt_plants_nounc6)[15] <- "Cr"
colnames(dt_plants_nounc6)[16] <- "Mn"
colnames(dt_plants_nounc6)[17] <- "Fe"
colnames(dt_plants_nounc6)[18] <- "Cu"
colnames(dt_plants_nounc6)[19] <- "Zn"
colnames(dt_plants_nounc6)[20] <- "As"
colnames(dt_plants_nounc6)[21] <- "Se"
colnames(dt_plants_nounc6)[22] <- "Cd"
colnames(dt_plants_nounc6)[23] <- "Re"

6 Data Visualization

6.1 Boxplots - Cu - All plots

Cu_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Cu_concentration, FUN = median), y = Cu_concentration, group=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  #theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 0))+
  geom_jitter(aes(colour = Plot), size=1) +
  ylim(0,600)+
  coord_flip()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Cu_AllPlots

6.2 Boxplots - Re - All Plots

Re_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Re_concentration, FUN = median), y = Re_concentration, group=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  #theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 0))+
  geom_jitter(aes(colour = Plot), size=1) +
  #ylim(0,600)+
  coord_flip()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Re_AllPlots

6.3 Boxplots - Re - Selected species

Re_box <- ggplot(Re_best, aes(x = reorder(Scientific_Name, Re_concentration, FUN = median), y = Re_concentration, fill=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 45))+
  geom_jitter(color="#85b8bc", size=2, alpha=0.9) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  #scale_fill_manual(values = c("", "", "", "", "", "","" ))
  scale_fill_manual(values = c("#4b2866", "#c7abdd", "#a578c9", "#381e4c", "#8347b2", "#5d327f","#251433" ))
  #scale_fill_brewer(palette = "Greens")

Re_box

6.4 Boxplots - Zn - All Plots

Zn_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Zn_concentration, FUN = median), y = Zn_concentration, group=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  #theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 0))+
  geom_jitter(aes(colour = Plot), size=1) +
  #ylim(0,600)+
  coord_flip()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Zn_AllPlots

6.5 Boxplots - Se - All Plots

Se_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Se_concentration, FUN = median), y = Se_concentration, group=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  #theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 0))+
  geom_jitter(aes(colour = Plot), size=1) +
  ylim(0,60)+
  coord_flip()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Se_AllPlots

6.6 Boxplots - Se - Selected species

Se_box <- ggplot(Se_best, aes(x = reorder(Scientific_Name, Se_concentration, FUN=median), y = Se_concentration, fill=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 45))+
  geom_jitter(color="#85b8bc", size=3, alpha=0.9) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  scale_fill_manual(values = c("#251433", "#c7abdd", "#8347b2"))
Se_box

6.7 Boxplots - Cu - Plot 6

Plants collected at the plot 6 were growing directly on the mine tailings that were exposed on the area of 100 x 100 m. Shrubs were also collected in the close vicinity to the tailings given their rooting depths.

Plot 6

Cu_Plot6 <- ggplot(P6, aes(x = reorder(Scientific_Name, Cu_concentration, FUN = median), y = Cu_concentration, group=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  #theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 0))+
  geom_jitter(aes(colour = Plot), size=1.6) +
  ylim(0,600)+
  coord_flip()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
  #scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E", "#38A6A5", "#73AF48", "#EDAD08", "#CC503E", "#38A6A5", "#73AF48", "#EDAD08"))
Cu_Plot6

7 Soil Table

8 PCA Analysis

8.1 Creating principal components

require(stats)
myPr1 <- prcomp(dt_plants_nounc1[,12:23], scale=TRUE)
myPr2 <- prcomp(dt_plants_nounc2[,12:23], scale=TRUE)
myPr5 <- prcomp(dt_plants_nounc5[,12:23], scale=TRUE)
myPr6 <- prcomp(dt_plants_nounc6[,12:23], scale=TRUE)
myPr15 <- prcomp(dt_plants_nounce15[,12:23], scale=TRUE)
myPr125 <- prcomp(dt_plants_nounce125[,12:23], scale=TRUE) # it was not working because the scale was FALSE

8.2 Biplots1

biplot(myPr1, scale=0)

biplot(myPr125, scale=0)

8.3 Biplots2 - Plot 1, 2 and 5

biplot125 <- biplot(myPr125,
             col=c('blue', 'red'),
             cex=c(0.8, 0.8),
             xlim=c(-.4, .4),
             main='PCA Results',
             expand=1.2)

8.4 Biplots2 - Plot 6

biplot6 <-  biplot(myPr6,
            col=c('blue', 'red'),
            cex=c(0.8, 0.8),
            xlim=c(-.4, .4),
            main='PCA Results',
            expand=1.2)

8.5 Bind dataframes with PC1 and PC2 for each plot

dt_plants1 <- cbind(dt_plants_nounc1, myPr1$x[,1:2])
dt_plants2 <- cbind(dt_plants_nounc2, myPr2$x[,1:2])
dt_plants5 <- cbind(dt_plants_nounc5, myPr5$x[,1:2])
dt_plants6 <- cbind(dt_plants_nounc6, myPr6$x[,1:2])
dt_plants15 <- cbind(dt_plants_nounce15, myPr15$x[,1:2])

8.6 PCA All plots

# Plot for all plot
myPr_all <- prcomp(dt_plants_nounc[,12:23], scale=TRUE)
dt_plants_all <- cbind(dt_plants_nounc, myPr_all$x[,1:2])

ggplot(dt_plants_all, aes(PC1, PC2, col=Plot, fill=Plot))+
  stat_ellipse(geom="polygon", col="black", alpha=0.5)+
  theme_classic()+
  geom_point(shape=21, col="black")

8.7 Variances across principle components

plot(myPr125, type="l")

summary(myPr1)

## Importance of components:
##                           PC1    PC2    PC3     PC4     PC5    PC6     PC7
## Standard deviation     1.7892 1.6134 1.2723 1.09515 1.00262 0.8604 0.71435
## Proportion of Variance 0.2668 0.2169 0.1349 0.09995 0.08377 0.0617 0.04252
## Cumulative Proportion  0.2668 0.4837 0.6186 0.71853 0.80230 0.8640 0.90652
##                            PC8     PC9   PC10    PC11    PC12
## Standard deviation     0.63622 0.59865 0.4436 0.30908 0.25739
## Proportion of Variance 0.03373 0.02987 0.0164 0.00796 0.00552
## Cumulative Proportion  0.94025 0.97012 0.9865 0.99448 1.00000

9 Partial Least Square Discriminant Analysis (PLS-DA)

library(readr)
library(dplyr)
library(tidyr)

## 
## Attaching package: 'tidyr'

## The following objects are masked from 'package:reshape':
## 
##     expand, smiths

## The following object is masked from 'package:reshape2':
## 
##     smiths

library(ropls)




dt_plants_nounc_3 <- dt_plants_nounc |> select(-Scientific_Name, -Group, -Plot, -Sample_Name, -Tube_No, -Type_of_Sample, -Cup_No, -pXRF_measurement_ID, -File, -Material)

typeof(dt_plants_nounc_3$Total_Weight)

## [1] "character"

dt_plants_nounc_3[,1] <- sapply(dt_plants_nounc_3[,1],as.numeric)

dt_nounc_PCA <- opls(x=dt_plants_nounc_3)

## PCA
## 226 samples x 13 variables
## standard scaling of predictors
##       R2X(cum) pre ort
## Total    0.558   3   0

plot(dt_nounc_PCA)

plot(dt_nounc_PCA, typeVc ="x-score", parAsColFcVn=dt_plants_nounc$Plot)

dt_opls <-opls(dt_plants_nounc_3, dt_plants_nounc$Plot)

## PLS-DA
## 226 samples x 13 variables and 1 response
## standard scaling of predictors and response(s)
##       R2X(cum) R2Y(cum) Q2(cum) RMSEE pre ort pR2Y  pQ2
## Total     0.59    0.179   0.122 0.398   4   0 0.05 0.05

summary(dt_opls)

## Length  Class   Mode 
##      1   opls     S4

##PCA

plot(dt_nounc_PCA, typeVc ="x-score", parAsColFcVn=dt_plants_nounc$Cu)

dt_opls <-opls(dt_plants_nounc_3, dt_plants_nounc$Cu)

## PCA
## 226 samples x 13 variables
## standard scaling of predictors
##       R2X(cum) pre ort
## Total    0.558   3   0

10 Redundancy Analysis (RDA) Biplot

dt_plants_trimmed <- dt_plants[c(-2,-4,-5,-6,-8,-10,-11, -24, -25, -40, -41, -42, -43, -44, -45, -seq(11,45,by=2))]
dt_plants_trimmed[,3] <- sapply(dt_plants_trimmed[,3],as.numeric)
library(vegan)

## Loading required package: permute

## Loading required package: lattice

## Registered S3 methods overwritten by 'vegan':
##   method      from
##   plot.rda    klaR
##   predict.rda klaR
##   print.rda   klaR

## This is vegan 2.6-4

# Create a matrix of the environmental variables (columns 5 to 18)
env_mat <- as.matrix(dt_plants_trimmed[,5:18])

# Create a data frame of the response variables (weight and thickness)
resp_df <- data.frame(weight = dt_plants_trimmed[,3], thickness = dt_plants_trimmed[,18])

# Perform RDA
rda_result <- rda(env_mat, resp_df)

# Print the RDA results
summary(rda_result)

## 
## Call:
## rda(X = env_mat, Y = resp_df) 
## 
## Partitioning of variance:
##                 Inertia Proportion
## Total         121406484    1.00000
## Constrained     1531857    0.01262
## Unconstrained 119874625    0.98738
## 
## Eigenvalues, and their contribution to the variance 
## 
## Importance of components:
##                            RDA1      RDA2       PC1       PC2       PC3
## Eigenvalue            1.510e+06 2.150e+04 1.177e+08 2.056e+06 9.631e+04
## Proportion Explained  1.244e-02 1.771e-04 9.696e-01 1.694e-02 7.933e-04
## Cumulative Proportion 1.244e-02 1.262e-02 9.822e-01 9.992e-01 1.000e+00
##                             PC4       PC5       PC6       PC7       PC8
## Eigenvalue            3.911e+03 8.272e+02 3.950e+02 1.908e+02 6.841e+01
## Proportion Explained  3.221e-05 6.813e-06 3.253e-06 1.572e-06 5.635e-07
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
##                             PC9      PC10      PC11
## Eigenvalue            1.485e+01 1.433e+01 5.563e+00
## Proportion Explained  1.223e-07 1.180e-07 4.582e-08
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00
## 
## Accumulated constrained eigenvalues
## Importance of components:
##                           RDA1      RDA2
## Eigenvalue            1.51e+06 2.150e+04
## Proportion Explained  9.86e-01 1.403e-02
## Cumulative Proportion 9.86e-01 1.000e+00
## 
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:  406.5426 
## 
## 
## Species scores
## 
##                        RDA1       RDA2        PC1        PC2        PC3
## Cl_concentration -1.118e+00 -2.6132416  3.913e+00 -5.290e+01 -3.458e-02
## Ca_concentration -4.527e+01  0.2982983  4.003e+02  5.182e-01 -7.806e-02
## Ti_concentration -1.996e-01 -0.8915015  4.554e-01 -3.743e-02  1.408e+00
## Cr_concentration  3.508e-03 -0.1006886  1.345e-02 -1.917e-02  5.380e-03
## Mn_concentration -4.304e-02  0.2687386  3.385e-01  5.274e-02 -1.114e-02
## Fe_concentration -2.259e+00 -4.6223101  2.690e+00 -1.426e-01  1.129e+01
## Ni_concentration  7.653e-04  0.0016839  4.491e-04  7.096e-04  7.266e-04
## Cu_concentration  1.159e-01 -0.2915491  2.933e-01 -9.958e-02  1.258e+00
## Zn_concentration  6.549e-02  0.1202517  4.283e-02 -1.816e-02 -2.305e-02
## As_concentration -9.716e-03 -0.0580838  9.539e-03 -5.080e-03  1.150e-02
## Se_concentration -3.236e-03  0.0208173  2.390e-02 -3.584e-03  3.048e-03
## Cd_concentration -1.077e-02 -0.0022981  7.833e-03 -1.386e-02 -5.672e-05
## Re_concentration -2.004e-03  0.0158494  5.890e-02  2.211e-03 -2.706e-02
## Substrate_RT      3.390e-04  0.0007463 -8.777e-20 -3.047e-20 -8.265e-20
##                         PC4
## Cl_concentration -3.409e-03
## Ca_concentration -3.582e-04
## Ti_concentration  4.713e-01
## Cr_concentration -7.629e-03
## Mn_concentration  3.109e-01
## Fe_concentration -3.046e-01
## Ni_concentration  3.964e-03
## Cu_concentration  2.213e+00
## Zn_concentration  1.224e-01
## As_concentration -4.268e-03
## Se_concentration  1.589e-02
## Cd_concentration -5.602e-04
## Re_concentration  3.057e-02
## Substrate_RT      5.996e-20
## 
## 
## Site scores (weighted sums of species scores)
## 
##            RDA1       RDA2        PC1        PC2       PC3      PC4
## sit1   -748.288  300.45656  86.041159   24.10835  48.47036 -38.5162
## sit2   -187.813  -40.15404  16.117741   26.87476  98.43987 -75.5024
## sit3   -727.041  322.94384  82.229273   26.35216  21.68294 -27.4388
## sit4   -524.078  -27.16805  62.738736  -15.11165  76.33183 -53.3936
## sit5   -331.634 -206.52794  35.002377  -56.45269   8.80916 -22.5313
## sit6    174.799  -33.40308 -25.770464   15.60207  -9.92309  -8.8928
## sit7   -650.223  143.24273  73.131123    3.41385  54.55562 -37.4839
## sit8   -386.750   96.39143  44.672582   25.25865  69.94003 -46.0739
## sit9   -745.184  497.69529  84.192780   37.02676 -47.60523  -7.9333
## sit10  -568.919  221.75330  63.664977   16.04563  18.87576  -4.0106
## sit11   -80.380  104.97444   3.526360   21.53936 -19.65551   1.5093
## sit12  -605.623  173.57852  66.536886   18.52929  52.72174 -42.6211
## sit13    -8.513   50.41842   3.435057    3.45504 -24.48045  -7.4393
## sit14  -148.519  185.93260  18.760273   18.37253 -21.00470  -7.2870
## sit15  -596.611   75.55122  70.295616  -38.93324 -34.89065 -14.3496
## sit16    79.231 -173.87110  -6.446185  -30.52783  -4.57959  15.0229
## sit17  -168.908  -52.48085  21.024237  -27.74468   0.20697  10.7570
## sit18     6.815 -310.59399   4.232789  -68.41588  -7.03167   5.9667
## sit19  -124.640 -326.82590  15.253766  -86.83163 -18.66540 -11.3164
## sit20   146.547 -245.73104 -15.937508  -42.37643 -26.34633   4.5219
## sit21   122.846 -132.78635 -10.834725  -18.25573 -11.18394  55.2803
## sit22   200.466  -64.34813 -18.069818    3.32289   4.75440  40.5778
## sit23  -615.596  271.16566  71.511922    2.46936  -5.17572 -12.4325
## sit24   259.177    0.85563 -29.577947   17.60335 -13.97539  -4.6871
## sit25   266.620    8.98485 -30.666277   20.92334  -9.31095  -7.2478
## sit26   258.435   -7.49668 -28.653557   17.94487  -8.53779  -8.4609
## sit27   153.246 -261.66388 -16.638184  -49.22149  -8.02597  -6.1885
## sit28    39.167  -53.33539  -6.107252  -16.73154  -6.69159  -4.5667
## sit29   150.806 -104.19684 -21.096727  -17.60074  -2.90268   2.2684
## sit30   124.056 -181.54589 -18.770932  -35.40456  -6.90481   0.8337
## sit31    99.008  -30.97522 -18.009435   -7.20354  -1.50738   4.1342
## sit32    76.988 -201.65480 -13.912810  -44.92209  -2.49988   7.6995
## sit33    33.707  152.78890   2.524112   27.78782  -8.83197 -15.0032
## sit34   154.415  100.16155 -16.685978   26.48838  -8.03291  -8.6156
## sit35   110.363  119.00874 -12.119048   27.35236 -12.48597  -5.6134
## sit36   -16.551  177.70702   1.354422   28.17276 -14.58806  -7.7783
## sit37    45.278   63.63532  -4.347834   18.72115   9.07470 -17.9615
## sit38   -49.401  131.62759   6.113839   28.52431  17.30283 -18.6096
## sit39   -24.380   87.24135   2.427142   19.73245   6.39496 -15.4812
## sit40   152.687 -248.14725 -14.741203  -45.53799 -10.15558 -14.3247
## sit41   249.785 -167.77662 -26.346705  -20.69576 -13.54343  -9.8384
## sit42   210.716 -331.60059 -20.130961  -57.99842  -8.90298 -15.7071
## sit43   245.374 -232.12551 -24.801573  -34.35524 -12.75188 -12.0942
## sit44   272.181   -7.43471 -31.403832   16.63648 -27.80567  -2.0533
## sit45   221.469 -147.03593 -22.182370  -19.30244  -3.69415 -13.6072
## sit46   264.622   -4.74984 -29.532091   15.37792 -11.60365  -3.8816
## sit47   258.371  -29.25715 -26.765124   12.05555 -11.32307  -9.2298
## sit48   212.701  -80.08197 -20.256920   -2.60538   4.17146 -10.1871
## sit49   224.537  -67.66907 -23.569622   -2.64693  -5.02341 -11.2431
## sit50   226.077  -54.12527 -20.259016    0.06004   0.34368 -16.2590
## sit51   -96.810  205.72062  12.250673   27.16387   3.38871  -7.9124
## sit52  -132.800  220.36496  10.831780   27.35963   1.21613  -5.3263
## sit53   -50.980  181.95707  -2.888454   25.35997  12.00751  -2.4280
## sit54  -142.930  224.85085  19.063073   29.57447 -12.61085  -4.4836
## sit55  -140.738  223.06683   9.242539   25.96804  12.56010   3.2297
## sit56   -51.756  182.11702   1.185210   26.75998   3.04667  -1.0673
## sit57  -234.242  150.40614  23.074373   25.35972   9.53387  51.1322
## sit58  -269.612  225.65484  33.483273   31.39909   9.33721  82.7049
## sit59  -158.872  157.08838  21.875580   28.94439  34.80909  62.7164
## sit60  -159.965  106.20688  20.575863   19.49614  11.83958   6.4980
## sit61   193.702 -170.86767 -19.505826  -26.16411  -5.81701 -10.8893
## sit62   206.494  -78.41755 -20.414612   -4.70982   0.26734 -10.2154
## sit63   206.024  -32.00430 -19.884322    5.42725   0.81945 -12.4216
## sit64   212.724 -182.92100 -20.696022  -27.45613   0.75272 -15.5415
## sit65   241.980 -158.57763 -24.969582  -19.81113  -2.20130 -10.0741
## sit66   246.615 -100.80957 -26.330919   -6.37332  -8.72407  -8.8134
## sit67   211.145 -178.23999 -22.005423  -26.91560  -7.24850 -11.6578
## sit68   191.902 -193.92546 -24.902517  -29.35027 -18.51768  -0.4056
## sit69   205.914 -137.29040 -21.843841  -17.25672  -3.85881  -9.0853
## sit70   217.233 -121.79368 -19.890729  -15.29298  -1.43271 -13.3713
## sit71   227.441 -127.28076 -21.159811  -14.88563  -3.44153 -13.9615
## sit72   167.869  -89.61862 -15.260086  -10.67650   0.40807  -8.2649
## sit73   255.065 -156.09183 -24.895549  -15.04885  -6.23792   3.7339
## sit74   240.603 -241.60657 -29.820213  -33.04971 -14.48961  11.3598
## sit75   187.265   -0.81771 -25.662064   13.87727 -21.84835   9.9688
## sit76    42.479   62.68688  -9.075301    6.55234   2.66782  -6.9099
## sit77   111.348  120.35204 -10.693135   25.80335  -0.06565 -11.6922
## sit78     8.398  165.92139   2.019080   26.14306   3.00624 -12.6842
## sit79   254.579  -79.59394 -34.615566   13.60594   5.80276 -13.7411
## sit80   245.156  -89.67004 -29.663812   10.15060   7.66462 -18.5437
## sit81   252.729 -117.32968 -33.206407   13.90277  28.98687 -14.2441
## sit82   255.212  -66.84026 -38.162957   21.25874  16.30463 -11.0192
## sit83   239.737  -81.73980 -26.026203   14.25518  27.86951 -28.7668
## sit84   253.566  -56.09576 -29.027590    8.82741 -18.19470  -7.0818
## sit85   262.550 -112.27131 -31.573637    8.12855   8.16954 -14.7728
## sit86   161.917 -119.65657 -18.029966   12.93695  61.87626 -14.8337
## sit87   254.209 -153.51181 -30.372792   -0.91158  17.05784 -23.9373
## sit88   242.813  -87.07001 -29.807868    8.76044   8.65178 -18.8549
## sit89   226.472 -218.74636 -31.997624    7.30947  72.30309 -52.9857
## sit90    20.703  101.51984  -1.332440   20.43590  -3.64103  -2.9770
## sit91   -82.389 -519.96531   7.300025 -117.54326  -6.43064 -10.9739
## sit92    55.422  127.54830  -6.940609   27.59761  -3.57134  -1.7760
## sit93   238.792  -10.46020 -27.024152   13.37932   2.03940   4.3624
## sit94   246.298    6.37027 -26.549628   15.13713  -5.35203  -8.0530
## sit95   238.625  -63.83978 -26.453954    0.19923  -3.35171  -8.2213
## sit96   245.749   -3.48213 -27.160029   14.29068 -12.16914  -6.9006
## sit97   201.290  -56.69459 -19.567087   -1.94614  -9.74758  -8.9149
## sit98   237.895 -110.90015 -26.744408   -9.54498  -5.42055  -9.8875
## sit99   245.540  -81.53818 -23.618211   -3.25286  -0.53172 -13.5478
## sit100  254.848  -35.89373 -25.465689    6.34791   5.48671 -12.8965
## sit101  214.273  -16.79464 -22.855333    6.25063   4.14047  -8.5796
## sit102  238.569   -4.04463 -23.223544   15.59292   1.03484   7.9953
## sit103  227.193   27.13234 -23.845995   20.87065  -5.78601  15.7749
## sit104  256.238  -24.82954 -27.221646   10.46128  -3.33351  -7.4014
## sit105  259.929   18.67681 -27.219621   19.35483  -2.47223  -9.5263
## sit106  264.234  -46.61973 -28.125225    6.08364 -12.41944  -7.8348
## sit107 -216.498    1.80233  24.074962    9.93871  48.03313 -35.8123
## sit108   91.988  -42.19449  -5.912381    3.68513  25.12218  -9.1438
## sit109   51.094   39.14833  -2.579173   13.30088   7.37183 -11.7534
## sit110   53.641   35.91342  -2.402636   11.18733   8.18624 -12.9741
## sit111   56.726   58.81808  -5.281617   20.07221  13.06854 -16.5787
## sit112  125.165   48.18890  -7.857260   15.04168   5.55472 -15.3621
## sit113  111.385   59.60788  -8.327878   17.66682   1.96552 -11.0302
## sit114   76.946   -2.37716  -8.084181   10.48797  36.57652  -2.2069
## sit115  -92.325  172.53034   8.511512   22.39173 -21.69025  -5.1730
## sit116  -15.117  167.60688   2.115569   28.56064 -12.34334  -9.7445
## sit117  165.616   90.91398 -20.225802   27.50555 -15.97260  -5.4895
## sit118   76.630  -85.25906  -6.637246  -15.11519  -1.02106   2.5869
## sit119   50.643   66.07586  -1.853287   11.27861  -2.26603  -3.7574
## sit120   85.444  -20.30047  -7.359657   -2.49753  -6.69355  -8.0563
## sit121  122.707  -98.19084 -12.221939  -14.99642 -15.89430  -0.8485
## sit122   84.227  -16.07175  -6.469645   -1.31703   0.55604  -8.6764
## sit123  108.977    0.03648  -9.800129    2.56670 -10.03996  -2.1925
## sit124  122.422 -148.45792 -10.885680  -27.55135  -0.33512  -6.8980
## sit125  121.144  -28.83753 -10.160265   -3.30702  -1.22049  -7.8079
## sit126  -82.445  -54.50279   7.670754  -22.03613   9.18441  -1.5559
## sit127   93.166 -157.04570  -7.991360  -14.53331  26.57292  -8.7169
## sit128   77.593  -28.01550  -8.587916    0.67307  16.22716   4.3074
## sit129  104.753 -258.59775 -10.850356  -41.37221  27.91371 -12.3642
## sit130   96.109 -207.14543  -9.782928  -27.85297  34.18142  -8.6709
## sit131   36.396   24.90795  -4.617700    0.78255 -15.08510  -8.1206
## sit132  -52.304  112.75630   2.592293   12.45947  -2.65620  -6.3511
## sit133   38.252   76.02191  -7.037579   10.74776   0.16535  -4.2906
## sit134  -50.408   54.81634   2.861190   -0.63958   3.59544  -2.5988
## sit135   67.996   73.39566  -9.258813   12.70002  12.57049  -3.9510
## sit136  -16.726  106.62242   3.492950   10.72180  11.71485  -8.9775
## sit137 -433.520  240.23359  47.089023   13.76842 -23.58467   7.0546
## sit138 -482.089  276.02866  52.746207   15.05392  -5.98468   6.0943
## sit139 -201.921   96.58420  21.005865   -2.08384 -26.57443  -5.0691
## sit140 -132.167   24.03040  11.101474  -10.31347 -32.31624  -2.5416
## sit141 -148.404   64.52020  14.912086   -4.80104 -21.38739   2.8086
## sit142 -516.542  311.34034  54.577673   16.56418 -18.03473  21.1904
## sit143 -202.392   66.37729  21.998787   -6.09403 -13.37921   6.2058
## sit144  -14.029   60.14635   1.625452    4.54566  -5.72596   1.8197
## sit145  -38.472   85.49532   2.588156   10.12348 -27.49221   7.7807
## sit146 -123.347  126.68213  13.823415   11.81992 -12.60819   2.1429
## sit147 -144.389   95.91495  14.232097    5.49700   4.73625  10.9238
## sit148  -10.954   82.17835  -3.015140   12.58333 -31.83507  12.4073
## sit149  -31.442   61.04834   6.515954    3.70180  -3.88126  13.6804
## sit150   -9.461   77.86515   6.594701   10.32944 -17.98949  -2.3436
## sit151  -66.417  137.05567   5.817779   17.18569 -18.97604   9.3211
## sit152  -45.717  185.38093   2.658559   27.99957  -9.73912  12.1573
## sit153 -145.550  172.93644  13.787954   15.39409  -9.28190  31.1128
## sit154 -266.423  137.59927  32.024667   -1.30952  -5.39993  21.0686
## sit155 -169.225  165.31598  18.986389   12.17421 -12.90751  20.0969
## sit156 -155.777  143.72281  18.691804    9.99572  -4.53641  45.7423
## sit157 -184.835  128.85486  19.861858    5.17628 -21.84975  37.0437
## sit158   17.739   -0.53960   0.014841   -5.93332  -4.95943   9.9670
## sit159  -46.025  127.81802   4.759508   17.10916  -5.09428  26.2970
## sit160 -224.803  233.09066  27.490741   23.19350  -9.51961  53.1410
## sit161  -79.089 -221.14105   6.679470  -50.73995 -18.33752  -7.0746
## sit162  -45.248 -369.96358   1.804031  -83.38880 -32.14035  -7.0966
## sit163   11.465  -69.91497  -5.047810    5.03074  16.59717 -31.2890
## sit164 -194.208  156.78833  22.705748   22.39116  11.41905   6.4365
## sit165 -852.882  310.33026  97.810182  -13.15595 -36.43033  -5.9120
## sit166 -839.543   98.29933  96.835422  -58.20712 -28.36290 -10.4112
## sit167 -774.209  240.13430  90.925602  -19.86314 -19.48343  -9.6463
## sit168  -99.081  215.43019   7.600086   29.16485 -20.68588  -1.3351
## sit169  -10.739  174.80713   0.009782   28.98806 -21.72113  -1.9512
## sit170 -371.775  340.18118  39.066487   31.84536 -30.02903  -1.5525
## sit171  -62.692  108.71662   4.068208   14.08929 -39.81954  10.0865
## sit172   32.146   52.74264   2.292207   16.92975   4.09174 -10.6937
## sit173  180.069   25.86089 -24.429569   22.01992 -27.03666  -0.5598
## sit174  304.539   33.68750 -39.619055   29.19337 -41.65585   6.9115
## sit175  205.110   18.01443 -23.390911   20.96546 -18.22905  -0.7580
## sit176  216.766  -36.42936 -25.582320   12.62906 -14.98924  10.3041
## sit177   96.517   17.89711  -6.503890    8.62349   7.63677   1.2157
## sit178  139.581   53.58411 -18.333495   19.98780 -32.10103   3.9865
## sit179   56.128  131.97119  -2.312555   27.46073  -3.27428  -4.5116
## sit180  173.284  -53.80066 -20.027045    0.76186 -18.49276  12.9132
## sit181  127.231 -100.40181 -16.622612   -3.39158  -7.88948  48.0986
## sit182  284.129   39.43733 -37.102009   29.28574 -39.68111   9.2589
## sit183  109.245 -569.51000 -17.146464  -66.20047  98.65932 105.2963
## sit184  111.358 -484.02872 -17.658369  -53.00357  86.13424  56.0367
## sit185  129.431 -360.43477 -16.694791  -48.24575  23.37100  39.7338
## sit186  118.838 -432.75452 -13.687153    4.21628 205.90425  68.4184
## sit187   99.048 -341.05228 -12.790794   -9.74765 118.86312 -21.2562
## sit188 -340.331  286.77348  38.465005   26.71037 -33.26481  22.5049
## sit189 -328.579  313.48043  41.205348   31.68503 -22.21377  20.8197
## sit190 -486.852  386.50857  53.471437   31.64695 -18.27292  21.9065
## sit191    1.986    8.86899  -0.716648    2.63793 -18.25159 -11.4839
## sit192   29.047   84.00206  -3.226319   21.87481  -5.57393 -13.4324
## sit193 -202.868 -659.07832  19.944600 -156.60820 -11.16897  -8.6405
## sit194  -17.621 -114.89330   2.904159  -20.30124  -4.10274 -20.8865
## sit195 -119.790   25.87020  11.473843   -1.92861   1.48166  -9.4709
## sit196 -222.003  -85.96149  21.743092  -32.69740   1.64625  -2.6726
## sit197 -173.736   -4.96110  19.476734  -15.38109   0.89773 -11.5852
## sit198 -305.814   14.42813  33.649070  -12.06702  21.64288 -15.4412
## sit199  120.270  -20.35288 -16.203925    7.91288 -11.57844  -7.0135
## sit200  -27.124   63.02206   1.226710   12.00154  -0.59756  -8.8178
## sit201    5.747   25.83490  -3.945567    7.45852   0.42666  -7.0758
## sit202 -114.651   -6.65658  11.873439  -12.63759 -10.19570 -11.7620
## sit203  123.662  -38.76633 -17.275638    8.04522 -12.76736  -7.3408
## sit204   54.306   30.37822  -7.467265   14.90368  -9.45420 -12.8983
## sit205  111.856 -331.80323 -17.648094  -65.74958 -18.14612   1.9109
## sit206   24.781   57.33488  -4.329275   22.09204  -1.71271  -8.7424
## sit207 -125.095 -146.31252  12.132166  -39.31498  -0.97049   1.8407
## sit208 -148.572   29.69314  15.191323   -6.64274  -4.08982   1.7061
## sit209 -286.008   43.50456  30.292833  -23.54810  -8.26241  12.7457
## sit210 -664.639   66.90110  67.927235  -53.58724 -13.49159  19.9997
## sit211   -1.275 -100.45951  -0.155144  -10.64242   5.36228 -20.0842
## sit212   -2.049  -67.26187   3.639454  -16.35456   4.90268   5.2059
## sit213  104.498  -90.24505 -11.660085  -13.67643 -21.72238  13.6856
## sit214   -3.448   11.14001   5.014643   -1.24465   1.22981  -6.9281
## sit215   33.437   15.92728  -0.875059    2.78651   0.86674  17.4551
## sit216   49.682  -58.90628  -3.856863   -9.79648 -10.87969  20.9930
## sit217   55.522    4.38652  -5.814775   -0.95951 -11.04774  -4.9160
## sit218  -38.059   50.75543   4.097134    9.07128  -6.30040  17.5248
## sit219   23.831    3.85582   2.943887    1.48965   5.26501  37.0660
## sit220   39.686   10.00863  -5.029459    0.72998 -11.09028  13.9031
## sit221  -24.374   25.80965   8.669958   11.44561  29.93242 208.1649
## sit222   28.567   48.86093  -5.432714   16.57377  -2.17726 163.0368
## sit223    9.200    0.14379  -3.906001   -4.69072  -9.96570  11.4926
## sit224   23.020   51.43123  -4.507207    5.15230 -10.54634  -7.8491
## sit225  205.962   -3.21608 -20.305432    7.31003  13.41509 -16.0238
## sit226  126.286  108.51427  -9.182775   25.41918   6.86929 -18.1483
## 
## 
## Site constraints (linear combinations of constraining variables)
## 
##            RDA1     RDA2        PC1        PC2       PC3      PC4
## con1    10.6276  -7.7079  86.041159   24.10835  48.47036 -38.5162
## con2   -45.0506 -40.3300  16.117741   26.87476  98.43987 -75.5024
## con3    -2.0746 -32.7878  82.229273   26.35216  21.68294 -27.4388
## con4    30.3252 -21.1917  62.738736  -15.11165  76.33183 -53.3936
## con5   -21.8543 -36.8509  35.002377  -56.45269   8.80916 -22.5313
## con6   -52.8478 -41.2367 -25.770464   15.60207  -9.92309  -8.8928
## con7    -4.7349 -12.9640  73.131123    3.41385  54.55562 -37.4839
## con8     7.5466 -41.3908  44.672582   25.25865  69.94003 -46.0739
## con9    -3.8419 -42.6501  84.192780   37.02676 -47.60523  -7.9333
## con10   -7.5320 -29.0801  63.664977   16.04563  18.87576  -4.0106
## con11  -49.8698 -45.2584   3.526360   21.53936 -19.65551   1.5093
## con12  -18.5720 -35.8922  66.536886   18.52929  52.72174 -42.6211
## con13   21.4605 -21.9911   3.435057    3.45504 -24.48045  -7.4393
## con14   16.3842  -1.2022  18.760273   18.37253 -21.00470  -7.2870
## con15   23.6414 -34.2240  70.295616  -38.93324 -34.89065 -14.3496
## con16   22.8607 -15.5595  -6.446185  -30.52783  -4.57959  15.0229
## con17   16.9882  11.0919  21.024237  -27.74468   0.20697  10.7570
## con18   45.2643 -12.0167   4.232789  -68.41588  -7.03167   5.9667
## con19   11.2084   2.1087  15.253766  -86.83163 -18.66540 -11.3164
## con20    6.4511 -39.7303 -15.937508  -42.37643 -26.34633   4.5219
## con21   27.4698 -30.6658 -10.834725  -18.25573 -11.18394  55.2803
## con22   41.0835 -12.1442 -18.069818    3.32289   4.75440  40.5778
## con23   14.9744  14.7839  71.511922    2.46936  -5.17572 -12.4325
## con24   -2.1292 -11.1524 -29.577947   17.60335 -13.97539  -4.6871
## con25   -4.2909  -6.4140 -30.666277   20.92334  -9.31095  -7.2478
## con26    5.3361 -13.8142 -28.653557   17.94487  -8.53779  -8.4609
## con27    7.2918  12.2618 -16.638184  -49.22149  -8.02597  -6.1885
## con28  -14.4701  34.3125  -6.107252  -16.73154  -6.69159  -4.5667
## con29  -34.9707  43.5450 -21.096727  -17.60074  -2.90268   2.2684
## con30  -40.9395  35.8162 -18.770932  -35.40456  -6.90481   0.8337
## con31  -59.7101  59.8579 -18.009435   -7.20354  -1.50738   4.1342
## con32  -44.9519  52.6077 -13.912810  -44.92209  -2.49988   7.6995
## con33   55.3919  -3.7384   2.524112   27.78782  -8.83197 -15.0032
## con34    6.7137  14.6096 -16.685978   26.48838  -8.03291  -8.6156
## con35    2.8745   5.6754 -12.119048   27.35236 -12.48597  -5.6134
## con36   -5.2535  12.7277   1.354422   28.17276 -14.58806  -7.7783
## con37    6.7116   6.0521  -4.347834   18.72115   9.07470 -17.9615
## con38    4.2111   8.2819   6.113839   28.52431  17.30283 -18.6096
## con39   -3.2458  -2.2803   2.427142   19.73245   6.39496 -15.4812
## con40   23.3781  -1.6473 -14.741203  -45.53799 -10.15558 -14.3247
## con41   17.6541  -7.4425 -26.346705  -20.69576 -13.54343  -9.8384
## con42   34.1069  -5.8276 -20.130961  -57.99842  -8.90298 -15.7071
## con43   27.1177 -10.4740 -24.801573  -34.35524 -12.75188 -12.0942
## con44   -5.3595 -34.4539 -31.403832   16.63648 -27.80567  -2.0533
## con45   26.1459  11.2128 -22.182370  -19.30244  -3.69415 -13.6072
## con46    3.7853  -1.8731 -29.532091   15.37792 -11.60365  -3.8816
## con47   21.9998 -19.3922 -26.765124   12.05555 -11.32307  -9.2298
## con48   34.1501   6.6236 -20.256920   -2.60538   4.17146 -10.1871
## con49   16.6731  13.5362 -23.569622   -2.64693  -5.02341 -11.2431
## con50   47.4213  13.2304 -20.259016    0.06004   0.34368 -16.2590
## con51   10.7940  42.3793  12.250673   27.16387   3.38871  -7.9124
## con52  -37.7393  57.0639  10.831780   27.35963   1.21613  -5.3263
## con53  -76.7693  93.5442  -2.888454   25.35997  12.00751  -2.4280
## con54   24.5370  -1.8871  19.063073   29.57447 -12.61085  -4.4836
## con55  -59.5483  92.0520   9.242539   25.96804  12.56010   3.2297
## con56  -41.7389  56.4592   1.185210   26.75998   3.04667  -1.0673
## con57  -31.1120 -29.4852  23.074373   25.35972   9.53387  51.1322
## con58   25.1934 -17.9472  33.483273   31.39909   9.33721  82.7049
## con59   33.8900  10.3464  21.875580   28.94439  34.80909  62.7164
## con60   21.2772 -32.1930  20.575863   19.49614  11.83958   6.4980
## con61   22.0787   7.4190 -19.505826  -26.16411  -5.81701 -10.8893
## con62   26.5463  12.1008 -20.414612   -4.70982   0.26734 -10.2154
## con63   30.5821   9.5266 -19.884322    5.42725   0.81945 -12.4216
## con64   30.7006  17.3176 -20.696022  -27.45613   0.75272 -15.5415
## con65   22.0989  14.0688 -24.969582  -19.81113  -2.20130 -10.0741
## con66   14.4234   0.5148 -26.330919   -6.37332  -8.72407  -8.8134
## con67   17.4766   9.0182 -22.005423  -26.91560  -7.24850 -11.6578
## con68  -27.3982  -6.2776 -24.902517  -29.35027 -18.51768  -0.4056
## con69   13.5366   9.7630 -21.843841  -17.25672  -3.85881  -9.0853
## con70   42.0740  14.2614 -19.890729  -15.29298  -1.43271 -13.3713
## con71   41.0614   6.8777 -21.159811  -14.88563  -3.44153 -13.9615
## con72   33.4855  12.1454 -15.260086  -10.67650   0.40807  -8.2649
## con73   35.7050 -14.1573 -24.895549  -15.04885  -6.23792   3.7339
## con74  -21.9623 -13.4527 -29.820213  -33.04971 -14.48961  11.3598
## con75  -39.5310 -22.8150 -25.662064   13.87727 -21.84835   9.9688
## con76  -37.6406  66.9039  -9.075301    6.55234   2.66782  -6.9099
## con77   16.5961  33.1185 -10.693135   25.80335  -0.06565 -11.6922
## con78   25.7832  41.3046   2.019080   26.14306   3.00624 -12.6842
## con79  -50.8672 -19.3308 -34.615566   13.60594   5.80276 -13.7411
## con80  -16.5377 -25.9330 -29.663812   10.15060   7.66462 -18.5437
## con81  -40.0456 -20.8854 -33.206407   13.90277  28.98687 -14.2441
## con82  -81.5423 -12.3700 -38.162957   21.25874  16.30463 -11.0192
## con83   10.2726 -11.9860 -26.026203   14.25518  27.86951 -28.7668
## con84   -2.7763 -35.8977 -29.027590    8.82741 -18.19470  -7.0818
## con85  -15.9481 -31.7450 -31.573637    8.12855   8.16954 -14.7728
## con86    3.3543  -7.5731 -18.029966   12.93695  61.87626 -14.8337
## con87  -13.4407 -17.6375 -30.372792   -0.91158  17.05784 -23.9373
## con88  -20.1168 -14.4738 -29.807868    8.76044   8.65178 -18.8549
## con89  -55.0452 -14.7904 -31.997624    7.30947  72.30309 -52.9857
## con90    8.5570   1.6363  -1.332440   20.43590  -3.64103  -2.9770
## con91  -16.0151   2.7605   7.300025 -117.54326  -6.43064 -10.9739
## con92   -6.3084  12.2706  -6.940609   27.59761  -3.57134  -1.7760
## con93    0.2506  18.7842 -27.024152   13.37932   2.03940   4.3624
## con94   11.8363  12.3542 -26.549628   15.13713  -5.35203  -8.0530
## con95    5.2905  16.5208 -26.453954    0.19923  -3.35171  -8.2213
## con96    5.8455  -4.3583 -27.160029   14.29068 -12.16914  -6.9006
## con97   28.6603  -0.4923 -19.567087   -1.94614  -9.74758  -8.9149
## con98    2.1474  12.6762 -26.744408   -9.54498  -5.42055  -9.8875
## con99   37.3065  11.1060 -23.618211   -3.25286  -0.53172 -13.5478
## con100  30.2180  28.1613 -25.465689    6.34791   5.48671 -12.8965
## con101  12.6488  36.9743 -22.855333    6.25063   4.14047  -8.5796
## con102  33.4939   0.2650 -23.223544   15.59292   1.03484   7.9953
## con103  16.4599  -4.0944 -23.845995   20.87065  -5.78601  15.7749
## con104  15.9534   8.9394 -27.221646   10.46128  -3.33351  -7.4014
## con105  19.5167  12.1887 -27.219621   19.35483  -2.47223  -9.5263
## con106  15.9607  -5.8407 -28.125225    6.08364 -12.41944  -7.8348
## con107  -3.8223 -36.2832  24.074962    9.93871  48.03313 -35.8123
## con108  40.0554   5.8596  -5.912381    3.68513  25.12218  -9.1438
## con109  28.1997  -2.0934  -2.579173   13.30088   7.37183 -11.7534
## con110  32.3494   5.8811  -2.402636   11.18733   8.18624 -12.9741
## con111   9.9421   4.9547  -5.281617   20.07221  13.06854 -16.5787
## con112  55.6730  13.0502  -7.857260   15.04168   5.55472 -15.3621
## con113  37.6573   6.7557  -8.327878   17.66682   1.96552 -11.0302
## con114   5.8621  41.6123  -8.084181   10.48797  36.57652  -2.2069
## con115 -17.8845  -1.7370   8.511512   22.39173 -21.69025  -5.1730
## con116   2.9111   2.4966   2.115569   28.56064 -12.34334  -9.7445
## con117 -13.4068  -2.5705 -20.225802   27.50555 -15.97260  -5.4895
## con118  18.3482   6.2042  -6.637246  -15.11519  -1.02106   2.5869
## con119  34.0780  14.1838  -1.853287   11.27861  -2.26603  -3.7574
## con120  20.5124   3.3048  -7.359657   -2.49753  -6.69355  -8.0563
## con121  15.0136 -16.3297 -12.221939  -14.99642 -15.89430  -0.8485
## con122  27.2040  12.2361  -6.469645   -1.31703   0.55604  -8.6764
## con123  22.3975   1.2268  -9.800129    2.56670 -10.03996  -2.1925
## con124  26.9032  17.5814 -10.885680  -27.55135  -0.33512  -6.8980
## con125  31.5885  17.9705 -10.160265   -3.30702  -1.22049  -7.8079
## con126 -14.3093  41.5419   7.670754  -22.03613   9.18441  -1.5559
## con127  23.2344 -13.4719  -7.991360  -14.53331  26.57292  -8.7169
## con128   2.0194  25.6764  -8.587916    0.67307  16.22716   4.3074
## con129  10.0929  24.9130 -10.850356  -41.37221  27.91371 -12.3642
## con130  10.6934  19.5971  -9.782928  -27.85297  34.18142  -8.6709
## con131  -4.5001   8.8413  -4.617700    0.78255 -15.08510  -8.1206
## con132 -29.6879  40.6689   2.592293   12.45947  -2.65620  -6.3511
## con133 -23.9981  48.8653  -7.037579   10.74776   0.16535  -4.2906
## con134 -25.1239  55.1197   2.861190   -0.63958   3.59544  -2.5988
## con135 -13.7434  66.9157  -9.258813   12.70002  12.57049  -3.9510
## con136  14.0214  65.6098   3.492950   10.72180  11.71485  -8.9775
## con137 -18.7370 -23.1078  47.089023   13.76842 -23.58467   7.0546
## con138 -17.2474  19.3298  52.746207   15.05392  -5.98468   6.0943
## con139 -16.9150 -12.1086  21.005865   -2.08384 -26.57443  -5.0691
## con140 -34.4262 -23.4455  11.101474  -10.31347 -32.31624  -2.5416
## con141 -17.0357  -2.9267  14.912086   -4.80104 -21.38739   2.8086
## con142 -35.7113  20.2049  54.577673   16.56418 -18.03473  21.1904
## con143  -8.4219  -1.3740  21.998787   -6.09403 -13.37921   6.2058
## con144   0.1651  22.1705   1.625452    4.54566  -5.72596   1.8197
## con145 -16.1213 -22.3560   2.588156   10.12348 -27.49221   7.7807
## con146  -1.7807   1.6117  13.823415   11.81992 -12.60819   2.1429
## con147 -18.9251  31.3292  14.232097    5.49700   4.73625  10.9238
## con148 -38.1120 -26.7069  -3.015140   12.58333 -31.83507  12.4073
## con149  25.9113  14.0894   6.515954    3.70180  -3.88126  13.6804
## con150  48.3271 -26.7798   6.594701   10.32944 -17.98949  -2.3436
## con151 -15.6181   1.0314   5.817779   17.18569 -18.97604   9.3211
## con152 -22.8709  25.6378   2.658559   27.99957  -9.73912  12.1573
## con153 -24.3356  36.7388  13.787954   15.39409  -9.28190  31.1128
## con154  15.9602  27.8934  32.024667   -1.30952  -5.39993  21.0686
## con155  -2.1435  20.6709  18.986389   12.17421 -12.90751  20.0969
## con156   8.8244  25.0714  18.691804    9.99572  -4.53641  45.7423
## con157 -10.0126  -2.0251  19.861858    5.17628 -21.84975  37.0437
## con158  17.9168  18.3966   0.014841   -5.93332  -4.95943   9.9670
## con159  -4.4157  20.8722   4.759508   17.10916  -5.09428  26.2970
## con160  17.1089  14.0033  27.490741   23.19350  -9.51961  53.1410
## con161 -19.4888 -35.6286   6.679470  -50.73995 -18.33752  -7.0746
## con162 -28.2199 -38.5365   1.804031  -83.38880 -32.14035  -7.0966
## con163 -32.9483 -46.4971  -5.047810    5.03074  16.59717 -31.2890
## con164   5.7627  -3.6592  22.705748   22.39116  11.41905   6.4365
## con165   9.5500 -14.8150  97.810182  -13.15595 -36.43033  -5.9120
## con166  15.1722   4.3365  96.835422  -58.20712 -28.36290 -10.4112
## con167  27.8100  -0.2929  90.925602  -19.86314 -19.48343  -9.6463
## con168 -32.7884  13.8440   7.600086   29.16485 -20.68588  -1.3351
## con169 -11.3937  -3.0801   0.009782   28.98806 -21.72113  -1.9512
## con170 -28.1264   4.8249  39.066487   31.84536 -30.02903  -1.5525
## con171 -27.4944 -43.7694   4.068208   14.08929 -39.81954  10.0865
## con172  52.1122 -27.1862   2.292207   16.92975   4.09174 -10.6937
## con173 -36.0523 -47.9333 -24.429569   22.01992 -27.03666  -0.5598
## con174 -45.8240 -51.5799 -39.619055   29.19337 -41.65585   6.9115
## con175  -1.7386 -37.9174 -23.390911   20.96546 -18.22905  -0.7580
## con176  -9.2322 -39.5724 -25.582320   12.62906 -14.98924  10.3041
## con177  39.0881  12.7660  -6.503890    8.62349   7.63677   1.2157
## con178 -22.7995 -39.6311 -18.333495   19.98780 -32.10103   3.9865
## con179  35.2184   4.1825  -2.312555   27.46073  -3.27428  -4.5116
## con180  -3.5534 -25.2950 -20.027045    0.76186 -18.49276  12.9132
## con181 -19.4104 -44.3196 -16.622612   -3.39158  -7.88948  48.0986
## con182 -44.0183 -50.4832 -37.102009   29.28574 -39.68111   9.2589
## con183 -39.7964 -18.8100 -17.146464  -66.20047  98.65932 105.2963
## con184 -42.5427 -16.3214 -17.658369  -53.00357  86.13424  56.0367
## con185 -16.7225 -33.3769 -16.694791  -48.24575  23.37100  39.7338
## con186   0.2383 -29.9440 -13.687153    4.21628 205.90425  68.4184
## con187 -12.2987 -33.1548 -12.790794   -9.74765 118.86312 -21.2562
## con188  -1.9391 -29.1677  38.465005   26.71037 -33.26481  22.5049
## con189  34.0137 -15.3841  41.205348   31.68503 -22.21377  20.8197
## con190 -16.0426  25.1058  53.471437   31.64695 -18.27292  21.9065
## con191  -4.5715 -34.6594  -0.716648    2.63793 -18.25159 -11.4839
## con192   0.1561 -19.0610  -3.226319   21.87481  -5.57393 -13.4324
## con193 -24.3495  -0.4621  19.944600 -156.60820 -11.16897  -8.6405
## con194   8.3111 -35.0923   2.904159  -20.30124  -4.10274 -20.8865
## con195 -18.5482   0.2488  11.473843   -1.92861   1.48166  -9.4709
## con196 -29.6588   1.1384  21.743092  -32.69740   1.64625  -2.6726
## con197  -1.6866   6.4300  19.476734  -15.38109   0.89773 -11.5852
## con198  -8.6106   1.1535  33.649070  -12.06702  21.64288 -15.4412
## con199 -22.8930 -25.6374 -16.203925    7.91288 -11.57844  -7.0135
## con200 -16.5187   1.2967   1.226710   12.00154  -0.59756  -8.8178
## con201 -29.1727   4.4393  -3.945567    7.45852   0.42666  -7.0758
## con202  -9.8226  -4.0574  11.873439  -12.63759 -10.19570 -11.7620
## con203 -28.9677 -42.9946 -17.275638    8.04522 -12.76736  -7.3408
## con204 -11.9048 -32.9040  -7.467265   14.90368  -9.45420 -12.8983
## con205 -42.8276   5.2914 -17.648094  -65.74958 -18.14612   1.9109
## con206 -13.8004 -36.4140  -4.329275   22.09204  -1.71271  -8.7424
## con207 -17.4239  -1.1967  12.132166  -39.31498  -0.97049   1.8407
## con208 -14.5278   3.5430  15.191323   -6.64274  -4.08982   1.7061
## con209 -18.5363  40.2783  30.292833  -23.54810  -8.26241  12.7457
## con210 -64.8027  73.2901  67.927235  -53.58724 -13.49159  19.9997
## con211  -2.3909 -39.0423  -0.155144  -10.64242   5.36228 -20.0842
## con212  30.3841   7.3855   3.639454  -16.35456   4.90268   5.2059
## con213   1.6674 -27.2578 -11.660085  -13.67643 -21.72238  13.6856
## con214  40.8130   2.6100   5.014643   -1.24465   1.22981  -6.9281
## con215  25.6738   6.2867  -0.875059    2.78651   0.86674  17.4551
## con216  15.7135 -19.9385  -3.856863   -9.79648 -10.87969  20.9930
## con217   4.1408   7.5678  -5.814775   -0.95951 -11.04774  -4.9160
## con218  -2.1590 -18.0860   4.097134    9.07128  -6.30040  17.5248
## con219  49.8083  -3.9328   2.943887    1.48965   5.26501  37.0660
## con220  -4.8069   3.2242  -5.029459    0.72998 -11.09028  13.9031
## con221  52.1201  -5.9690   8.669958   11.44561  29.93242 208.1649
## con222 -19.7263 -18.5373  -5.432714   16.57377  -2.17726 163.0368
## con223 -25.2783  17.7636  -3.906001   -4.69072  -9.96570  11.4926
## con224 -16.9301  22.6142  -4.507207    5.15230 -10.54634  -7.8491
## con225  26.9094  54.0742 -20.305432    7.31003  13.41509 -16.0238
## con226  44.9391  30.9500  -9.182775   25.41918   6.86929 -18.1483
## 
## 
## Biplot scores for constraining variables
## 
##               RDA1   RDA2 PC1 PC2 PC3 PC4
## weight    -0.09769 0.9952   0   0   0   0
## thickness  0.41362 0.9104   0   0   0   0

#Plot
# Plot the RDA biplot
plot(rda_result, scaling = 3)

# Add labels to the plot
text(rda_result, display = "species", col = "blue", cex = 0.8) #species - environmental variales
text(rda_result, display = "sites", col = "red", cex = 0.8) # the sites - response variables 

# Add a title to the plot
title(main = "Redundancy Analysis (RDA) Biplot")